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Revenue & Experimentation Engine

Case Study — Product Data Scientist Portfolio

90-Second Summary

Problem

Product teams need to model revenue impact and rollout risk before launching experiments. Poor experiment design leads to false positives and revenue leakage.

Approach

Built an interactive simulator that takes traffic, conversion, uplift, and ARPU as inputs and outputs expected revenue lift, confidence intervals, statistical significance, and a GO/ITERATE/KILL recommendation.

Output

Web-based engine with sliders for traffic, conversion %, uplift %, ARPU, duration, and segment. Real-time visualization of confidence band and risk meter.

Decision

Ship when significance ≥95%, false-positive risk <5%, and uplift ≥5%. Otherwise iterate or kill.

Measurement Plan

Time HorizonMilestoneSuccess Criteria
0–30 daysBaseline establishedPre-experiment conversion/ARPU measured; traffic logs validated
30–60 daysExperiment runMinimum sample size reached; no SRM detected
60–90 daysRollout decisionStatistical significance achieved; revenue impact quantified

Success Metrics & Tripwires

Success: Revenue lift >5% with p<0.05; false-positive rate <5%; no degradation in core retention.

Tripwire (anti-success): Sample ratio mismatch (SRM); cross-contamination between control/variant; external market events skewing results.

Instrumentation / Event Taxonomy

Event NameRequired PropertiesNotes
experiment_impressionexperiment_id, variant, user_idFired on assignment
experiment_conversionexperiment_id, variant, user_id, revenueConversion with revenue
experiment_sessionexperiment_id, variant, session_id, duration_secEngagement proxy

Data Model Layer

Staging: Raw event logs ingested into stg_experiment_events. Deduplication and timestamp normalization.

Marts: fct_experiment_results — experiment_id, variant, conversions, revenue, sample_size. fct_experiment_statistics — lift, p_value, confidence_interval.

Tests: Uniqueness on (experiment_id, user_id, variant); not_null on revenue for conversion events; referential integrity to experiment config.

Documentation: dbt docs; column-level descriptions; lineage to upstream sources.

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